#!/usr/bin/env python

import tensorflow as tf
import os

# The data in cancer.csv:
# 10,10,10,8,6,1,8,9,1,1
# 6,2,1,1,1,1,7,1,1,0
# 2,5,3,3,6,7,7,5,1,1


def convert_tfrecords(input_filename, output_filename):
  #current_path = os.getcwd()
  current_path = os.getcwd() + "/multiple_two/"
  #current_path = os.getcwd() + "/add_one/"

  input_file = os.path.join(current_path, input_filename)
  output_file = os.path.join(current_path, output_filename)
  print("Start to convert {} to {}".format(input_file, output_file))

  writer = tf.python_io.TFRecordWriter(output_file)

  for line in open(input_file, "r"):
    # Split content in CSV file
    data = line.split(",")
    label = float(data[9])
    features = [float(i) for i in data[0:9]]

    # Write each example one by one
    example = tf.train.Example(features=tf.train.Features(feature={
        "label":
        tf.train.Feature(float_list=tf.train.FloatList(value=[label])),
        "features":
        tf.train.Feature(float_list=tf.train.FloatList(value=features)),
    }))

    writer.write(example.SerializeToString())

  writer.close()
  print("Successfully convert {} to {}".format(input_file, output_file))


#current_path = os.getcwd()
current_path = os.getcwd() + "/multiple_two/"
#current_path = os.getcwd() + "/add_one/"

for file in os.listdir(current_path):
  if file.endswith(".csv") and not file.endswith(".tfrecords"):
    convert_tfrecords(file, file + ".tfrecords")
